1. Stat 414 – Day 22

2. Last Time – Pseudo R2
“The literature does not seem to have converged on this topic.”
Comparison of total variation to null model
Change in variance components when add variables

3. Day 20 handout What are the following 4 equations for na vs. mpqnem:
Orch = 0, Large performance
Orch = 1, Large performance
Orch = 0, Smaller performance
Orch = 1, Smaller performance

4. Multilevel Data (Table 2.2)
Teachers
Families
Employees
Teeth
Children
Animals
Patients
Measurements
Respondents
Suspects
Schools
Neighborhoods, States
Firms
Jawbones
Families
Litters
Doctors, Hospitals
Subjects
Interviewees
Judges

5. Other examples Level 1 Level 2 Yelp reviews Airbnb (prices)
Voter (why voted for T)
Year (financial aid)
Year (corruption index)
Year (happiness index)
Restaurant
Neighborhoods
Census tract, State
College
Country

6. Common applications Multistage sample Growth models
Random sample of clusters
Random sample of individuals within each cluster
Growth models
Community health survey stratified by neighborhood
California Cancer Registry within HMO

7. Typically Number of (level 2) groups is large
Level 2 units are considered random sample of larger population (come from a distribution)

8. Possibilities? Level 1 Level 2 Movie theater Movie patron Free agent
Revenue of Halloween
Movie patron
Movie rating
Free agent
Salary
Playoff game
Points scored
Price bottle of wine
City
Number theaters, distances
Theater
College town
Agent
Years experience, # clients
Year
TV market
Region

9. Possibilities? Level 1 Level 2 Olympic athletes Body fat percentages
Finishing time
Body fat percentages
Movie revenue, rating
Country (over time)
Time – athlete – country
Clinic
Location, SES
Genre, Year

10. Sports?
JSM 2018: Hierarchical or multilevel models can play an important role in player evaluation in team sports. In American football, Yurko et al (2018) present a hierarchical model for estimating wins above replacement (WAR) for offensive skill positions, complete with a full treatment of uncertainty similar to that of Baumer et al (2015), but for football and using a drive-based resampling approach. In hockey, Thomas et al (2013) present a hierarchical competing process model for offensive and defensive player ratings. We discuss these two papers and their extensions, including how National Football League (NFL) teams can use this approach to calculate WAR for players of all positions, and how subsequent improvements can potentially be made in player evaluation and strategy at the NFL Draft. We present results on the 2017 NFL season and provide a definitive answer, once and for all, to the question: "Is Joe Flacco elite?"

11. Possibilities? IPEDS – education data Census.gov FDA clinical trials
Data on individual schools but in different regions/counties/states
Census.gov
American Community Survey
PUMS
FDA clinical trials
Repeated measures
NCAA reference
Randomly sample by college